HPSIPT: A high-precision single-packet IP traceback scheme

Murugesan, Vijayalakshmi; Selvaraj, Mercy Shalinie; Yang, Ming Hour

doi:10.1016/j.comnet.2018.07.013

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…The results are similar for the data from measurement area B: the accuracies are 100% and 87.83% for damaged and undamaged data, respectively. To further quantify the performance of damage identification, three metrics, precision, accuracy and F 1 score, are introduced [36]; they are defined as follows:…”

Section: Scenario 1: Verification In Single Monitoring Areamentioning

confidence: 99%

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Feature Selection and Damage Identification for Urban Railway Track Using Bayesian Globally Sparse Principal Component Analysis

Huang

Chen

et al. 2023

Sustainability

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Urban railway track infrastructures often suffer from damage that affects their service performance due to a variety of factors. In this study, an unsupervised feature selection and damage identification method based on globally sparse probabilistic principal component analysis (PCA) is proposed for urban railway tracks using the monitoring data of train-induced dynamic responses. A Bayesian framework is proposed for generating principal components on a basis of vectors (original variables) with a global sparseness pattern instead of separate patterns in a traditional sparse PCA. In this framework, a variational expectation-maximization algorithm is employed to obtain the tractable calculation of the marginal likelihood function for learning all uncertain parameters of the Bayesian model. The obtained principal components are linear combinations of the very same set of important variables, making our method better interpretable than the traditional sparse PCA. We can clearly understand which original variables are most relevant for describing the data. The track damage is identified simply by discriminating the corresponding measured dynamic responses using the binary elements of the latent vector inferred from the Bayesian globally sparse PCA algorithm. The usefulness is demonstrated by successfully identifying the track bed plate crack damage through the actual train-induced dynamic responses collected from the structural health monitoring system of an urban railway track infrastructure, where the method is able to achieve F1 scores of 90% or higher for various scenarios.

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Section: Scenario 1: Verification In Single Monitoring Areamentioning

confidence: 99%

“…For a more intuitive comparison, we also present the results as a histogram in Figure 7. To further quantify the performance of damage identification, three met sion, accuracy and 𝐹 score, are introduced [36]; they are defined as follows:…”

Section: Scenario 1: Verification In Single Monitoring Areamentioning

confidence: 99%

Feature Selection and Damage Identification for Urban Railway Track Using Bayesian Globally Sparse Principal Component Analysis

Huang

Chen

et al. 2023

Sustainability

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“…( 6) Hybrid IP traceback schemes combine packet tagging and logging-based schemes. A highprecision single-packet IP traceback (HPSIPT) scheme [24] is a typical hybrid traceback scheme.…”

Section: Related Workmentioning

confidence: 99%

“…According to the above six common evaluation indexes, we compare the ten representative traceback schemes (ingress filtering [18], SPIE [22], PPM [20], iTrace [4], CenterTrack [23], HPSIPT [24], SDN traceroute [15], SDN selective logging method [17], SMITE [29], and cross-domain traceback scheme [16]). e comparison results are listed in Table 1.…”

Section: Related Workmentioning

confidence: 99%

A Low-Overhead and High-Precision Attack Traceback Scheme with Combination Bloom Filters

et al. 2022

Security and Communication Networks

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Distributed denial of service attacks seriously threatens the availability of highly resilient software-defined networking systems, such as data center networks. A traceback scheme is an effective means of mitigating attacks by identifying the location of the attacker and the attacking path. However, traditional traceback schemes suffer from low traceability success rates, high packet header overheads, and high communication traffic overheads, in addition to the fact that logically centralized traceability schemes make the control plane a prime target for attacks. To overcome the above challenges, we propose the low-overhead and high-precision traceback scheme, which is divided into two stages: packet marking and path reconstruction. The first stage of the traceback scheme utilizes programmable switches in the data plane to selectively mark the actual physical path information that the packet was forwarded on. The marking method is adaptive to the path length, which utilizes a combined Bloom filter so that the packet length does not grow with the length of the attacking path. The proposed probabilistic packet marking algorithm effectively reduces the number of packets collected to reconstruct the attacking path. The second stage of the traceback scheme utilizes the distributed victim host to reconstruct the attacking path without the controller and locate the source of the attacker. Theoretical analysis and experimental results show that the proposed scheme ensures the high accuracy of tracing and minimizes the traffic overhead and storage overhead required for the traceback process.

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A Multi Agent Framework to Detect in Progress False Data Injection Attacks for Smart Grid

Sinha

Thukkaraju

Vyas

2022

Communications in Computer and Information Science

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HPSIPT: A high-precision single-packet IP traceback scheme

Cited by 12 publications

References 28 publications

Feature Selection and Damage Identification for Urban Railway Track Using Bayesian Globally Sparse Principal Component Analysis

Feature Selection and Damage Identification for Urban Railway Track Using Bayesian Globally Sparse Principal Component Analysis

A Low-Overhead and High-Precision Attack Traceback Scheme with Combination Bloom Filters

A Multi Agent Framework to Detect in Progress False Data Injection Attacks for Smart Grid

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